ScaleOps
ScaleOps is an autonomous cloud and AI infrastructure optimization platform that continuously manages Kubernetes resources (CPU, memory, GPU, replicas, and nodes) in real time, cutting cloud costs by up to 80% without compromising performance or reliability.
Perfect for
ScaleOps is best suited for DevOps, platform engineering, and SRE teams running Kubernetes at scale who need to reduce cloud costs while maintaining or improving application performance and reliability.
- Organizations running Kubernetes on AWS EKS, Google GKE, Azure AKS, on-premises, or air-gapped environments
- Teams managing GPU and AI/ML inference workloads (LLM serving, model training, fine-tuning) that need fractional GPU optimization and dynamic resource allocation
- Enterprises in regulated industries (finance, healthcare, government) requiring self-hosted, FedRAMP-compatible, or FIPS-compliant deployments with no data egress
- Companies with multi-cluster environments where manual resource tuning does not scale and cost drift accumulates across dozens of clusters
- Platform teams already using HPA, VPA, KEDA, Karpenter, or Cluster Autoscaler who want intelligent automation layered on top without replacing existing tooling
Key strengths
Self-hosted deployment that runs entirely inside the customer's cluster with no data egress, supporting air-gapped and FedRAMP environments
Real-time, context-aware optimization that works alongside existing Kubernetes autoscalers (HPA, VPA, KEDA, Karpenter) without requiring replacements or manifest changes
Comprehensive GPU optimization with dynamic fractional GPU sharing, per-pod utilization-based scaling, and autonomous GPU workload rightsizing
Single Helm command installation with no cloud permissions required and immediate time-to-value
JVM-aware optimization that accounts for heap vs. non-heap memory and GC behavior, a capability competitors lack
Considerations
Primarily focused on Kubernetes environments; organizations not running Kubernetes workloads would not benefit
No publicly listed pricing, requiring a demo or sales engagement to understand cost
The platform's depth of Kubernetes-specific features may present a learning curve for teams new to Kubernetes resource management concepts
Free tier is limited to a 30-day proof-of-concept period, with no permanent free plan mentioned
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About ScaleOps
ScaleOps provides a fully autonomous Kubernetes resource management platform that eliminates the need for manual infrastructure tuning. The platform continuously analyzes and optimizes CPU, memory, GPU, storage, and network allocations across every application, AI model, and agent running in production. By operating as a self-hosted solution that runs entirely inside the customer's cluster, ScaleOps ensures that no data leaves the environment, making it suitable for regulated, air-gapped, and FedRAMP-certified deployments. Trusted by industry leaders including Salesforce, Wiz, DocuSign, DraftKings, H&M, Rubrik, ServiceNow, Unity, and Outbrain, ScaleOps is ranked as a market leader in autonomous cloud infrastructure optimization on G2 with a 4.8/5 rating.
The platform addresses the full spectrum of Kubernetes optimization challenges through a unified suite of capabilities: automated real-time pod rightsizing, replica optimization, smart pod placement, Spot instance optimization, node consolidation, and Karpenter optimization. For AI and GPU workloads specifically, ScaleOps offers autonomous GPU workload rightsizing with dynamic fractional GPU sharing, AI replica optimization based on real pod-level GPU utilization, and improved GPU availability through optimized GPU selection. The platform also includes an AI SRE Agent that connects to live clusters via MCP, enabling engineers to query cluster state, diagnose issues, and execute approved changes directly from their IDE or AI workflow tools like Claude, Cursor, and Codex.
ScaleOps differentiates itself from competitors by operating in real time with full application context awareness rather than relying on static rules or scheduled recommendations. It works alongside existing Kubernetes autoscalers (HPA, VPA, KEDA, Cluster Autoscaler, Karpenter) without requiring replacements or manifest changes, respecting PodDisruptionBudgets, rollout strategies, and RBAC policies. The platform installs with a single Helm command, requires no cloud permissions, and delivers immediate value. It supports JVM-aware optimization for Java workloads, boot-time CPU management for model serving pods, and comprehensive cost monitoring with per-workload spend breakdowns by cluster, namespace, team, or application.
ScaleOps
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